Learning discriminative dictionaries with partially labeled data

Shrivastava, Ashish; Pillai, Jaishanker K.; Patel, Vishal M.; Chellappa, Rama

doi:10.1109/icip.2012.6467559

Cited by 43 publications

(40 citation statements)

References 15 publications

(22 reference statements)

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“…[24], Shrivastava et al [19] learnt a class-specific dictionary by using Fisher discriminant analysis on the coding vectors of the labeled data. However, its model is complex: the training data is represented by a combination of all class-specific dictionaries, and the coding coefficients are regularized by both intra-class and inter-class constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Here, as in prevailing semi-supervised dictionary methods [11,18,19,[21][22][23]36], we assume that the unlabeled training data belongs to some class of the training set. In our proposed model, the dictionary to be…”

Section: Ssd-lp Modelmentioning

confidence: 99%

“…A semi-supervised class-specific dictionary has also been learned in Ref. [19]. However, its model is a little complex due to many regularizations.…”

Section: Introductionmentioning

confidence: 99%

“…Dictionary learning methods can be divided into three main categories: unsupervised [13], supervised [14][15][16][17], and semi-supervised [11,[18][19][20][21][22][23]. K-SVD [13] is a representative unsupervised dictionary learning model, which is widely applied to image restoration tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Semi-supervised dictionary learning [11,18,19,[21][22][23]36] has gained considerable interest in the past several years. In semi-supervised dictionary learning, the issue of whether the unlabeled samples can be accurately estimated for use as labeled samples for training is very important.…”

Section: Introductionmentioning

confidence: 99%

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Semi-supervised dictionary learning with label propagation for image classification

Chen

Yang

2017

Comp. Visual Media

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Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification, which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semisupervised dictionary learning method using label propagation (SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors simultaneously. Extensive experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Ssd-lp Modelmentioning

confidence: 99%

“…A semi-supervised class-specific dictionary has also been learned in Ref. [19]. However, its model is a little complex due to many regularizations.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semi-supervised dictionary learning with label propagation for image classification

Chen

Yang

2017

Comp. Visual Media

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Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra

Adebileje

Ghasemi

Aiyelabegan

et al. 2016

Magnetic Reson in Chemistry

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Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd.

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Introduction

Patel

Chellappa

2013

Sparse Representations and Compressive Sensing for Imaging and Vision

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Learning discriminative dictionaries with partially labeled data

Cited by 43 publications

References 15 publications

Semi-supervised dictionary learning with label propagation for image classification

Semi-supervised dictionary learning with label propagation for image classification

Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra

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